String similarity determination

ABSTRACT

A system and a method for determining a similarity between a first string and a second string. A sequence of edit operations are performed on the first string in order to obtain the second string may be determined. The edit operation is of a first type or a second type. The first type operation comprises a character insertion operation or character removal operation. The second type operation comprises a character maintenance operation. The first type edit operation is associated with an operation score indicative of a cost for applying the edit operation. The first type edit operation is associated with a switching score indicative whether it is immediately followed by a second type edit operation. The switching scores and/or operation scores associated with the sequence of edit operations are combined in order to obtain a combined score that is indicative of the similarity level between the first and second strings.

BACKGROUND

The present disclosure relates to the field of digital computer systems,and more specifically, to a method for determining a similarity betweentwo strings.

Record Linkage requires to link elements of a source dataset to relateddata items of a target dataset. For that a record matching may beperformed between records of the datasets. The record matching involvesthe computation of similarities between strings. However, there is acontinuous need to improve the distance measurements.

SUMMARY

Various embodiments provide a method for determining a similaritybetween two strings, computer system and computer program product asdescribed by the subject matter of the independent claims. Advantageousembodiments are described in the dependent claims. Embodiments of thepresent disclosure can be freely combined with each other if they arenot mutually exclusive.

In one aspect, the disclosure relates to a method for determining asimilarity between a string s₁ having N₁ characters, where N_(1≥)0 and astring s₂ having N₂ characters, where N₂≥0. The method comprises:

-   -   a. providing a distance algorithm being configured for:        -   i. receiving a first string and a second string;        -   ii. determining a sequence of one or more edit operations to            be performed on characters of the first string in order to            obtain the second string, the edit operation being of a            first type or a second type, the first type edit operation            comprising a character insertion operation or character            removal operation, the second type edit operation comprising            a character maintenance operation; wherein the first type            edit operation is associated with an operation score            indicative of a cost for applying the edit operation;            wherein the first type edit operation is associated with a            switching score indicative whether it is immediately            followed in the sequence by a second type edit operation;        -   iii. combining the switching scores and/or operation scores            associated with the sequence of edit operations, resulting            in a combined score that is indicative of the similarity            level between the first and second strings;    -   b. inputting first n₁ characters of the string s₁ as the first        string and first n₂ characters of the string s₂ as the second        string to the distance algorithm for obtaining the combined        score, wherein 0≤n₁≤N₁ and 0≤n₂≤N₂;    -   c. determining of the distance between the string s₁ and string        s₂ using the obtained combined score.

In another aspect, the disclosure relates to a computer program productcomprising a computer-readable storage medium having computer-readableprogram code embodied therewith, the computer-readable program codeconfigured to implement all of the steps of the method according topreceding embodiments.

In another aspect, the disclosure relates to a computer system fordetermining a similarity between a string s₁ having N₁ characters, whereN₁≥0 and a string s₂ having N₂ characters, where N₂≥0. The computersystem is configured for:

-   -   a. providing a distance algorithm being configured for:        -   i. receiving a first string and a second string;        -   ii. determining a sequence of one or more edit operations to            be performed on characters of the first string in order to            obtain the second string, the edit operation being of a            first type or a second type, the first type edit operation            comprising a character insertion operation or character            removal operation, the second type edit operation comprising            a character maintenance operation; wherein the first type            edit operation is associated with an operation score            indicative of a cost for applying the edit operation;            wherein the first type edit operation is associated with a            switching score indicative whether it is immediately            followed in the sequence by a second type edit operation;        -   iii. combining the switching scores and/or operation scores            associated with the sequence of edit operations, resulting            in a combined score that is indicative of the similarity            level between the first and second strings;    -   b. inputting first n₁ characters of the string s₁ as the first        string and first n₂ characters of the string s₂ as the second        string to the distance algorithm for obtaining the combined        score, wherein 0≤n₁≤N₁ and 0≤n₂≤N₂;    -   c. determining of the distance between the string s₁ and string        s₂ using the obtained combined score.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following embodiments of the disclosure are explained in greaterdetail, by way of example only, making reference to the drawings inwhich:

FIG. 1 is a block diagram of a computer system in accordance with anexample of the present subject matter.

FIG. 2 is a flowchart of a method for determining a similarity betweentwo strings in accordance with an example of the present subject matter.

FIG. 3 is a flowchart of a method for determining a similarity betweentwo strings in accordance with an example of the present subject matter.

FIG. 4 is a flowchart of a method for determining a similarity betweentwo strings in accordance with an example of the present subject matter.

FIG. 5A is a flowchart of a method for determining a similarity betweentwo strings in accordance with an example of the present subject matter.

FIG. 5B shows the evolution of the content of a matrix indicative ofedit distances in accordance with an example of the present subjectmatter.

FIG. 6A is a flowchart of a method for determining a similarity betweentwo strings in accordance with an example of the present subject matter.

FIG. 6B shows the evolution of the content of a matrix indicative ofedit distances in accordance with an example of the present subjectmatter.

FIG. 7 is a pseudocode for determining a similarity between two stringsin accordance with an example of the present subject matter.

FIG. 8 represents a computerized system, suited for implementing one ormore method steps as involved in the present disclosure.

DETAILED DESCRIPTION

The descriptions of the various embodiments of the present disclosurewill be presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The similarity between two strings may be measured by a distance betweenthe two strings. However, an accurate determination of the distance thatworks for a large variety of compared strings may be a challenging task.For that, the present subject matter may provide a string similarityfunction that provides a number indicating the presentalgorithm-specific indication of distance. Said number may be thecombined score as described herein. The term “string” as used herein maybe a sequence of zero or more characters, wherein a character may be anumber, a letter or any special character. An accurate determination ofthe similarity of two strings may have an impact on several fields ofapplications where the similarity analysis may be used. For example, thepresent string distance algorithm may be used in areas including frauddetection, fingerprint analysis, plagiarism detection, ontology merging,DNA analysis, RNA analysis, image analysis, evidence-based machinelearning, database data deduplication, data mining, incremental search,data integration, Malware Detection, semantic knowledge integration, andnatural language processing, where automatic spelling correction candetermine candidate corrections for a misspelled word by selecting wordsfrom a dictionary that have a low distance to the word in question.

The present subject matter may enable to compute an edit distance bycounting the number of edit operations required to transform one stringinto the other. The edit operations are scored depending on their types.In addition, the present subject matter may score the operations basedon their order of application. This may provide accurate comparisons andmay overcome the following issues of existing edit distance measures.For example, the Levenshtein similarity between “Textile” and “TextileCompany” is the same as between “Textile” and “TCeoxm tpialney” simplybecause “Company” inserted in junk has the same weight as the lettersinserted at random places. The same may occur for “Wachter AG” versus“Wechsler AG” and “Wachter Bau AG”. The Levenshtein similarity maypenalize word permutations quite heavily. For instance, “ChaussuresMichel” and “Michel Chassures” has a farther distance than “ChaussuresMichel” and “Chic Chaussures” simply because due to the permutation alot of insertion and removal operations are necessary.

The present subject matter may provide a distance algorithm. Thedistance algorithm may receive as input a first string and a secondstring and determine a sequence of one or more edit operations to beperformed on characters of the first string in order to obtain thesecond string. The distance algorithm may apply a score assignment rulefor scoring the sequence of one or more edit operations. The result ofthe scoring is the combined score. The combined score may be an editdistance between the first string and the second string. The editoperation may be of a first type or a second type. The first type editoperation comprises a character insertion operation (referred to by “I”)or character removal operation (referred to by “D”). The second typeedit operation comprises a character maintenance operation (referred toby “M”), e.g., the character maintenance operation is named “editoperation” for naming purpose only, as it may not involve editing. Thedistance algorithm may apply the score assignment rule on each operationof the sequence of operations. The score assignment rule assigns to thefirst type edit operations an operation score indicative of a cost forapplying the first type edit operation. The operation score may, forexample, be equal to one. The score assignment rule may assign to thesecond type edit operation an operation score equal to zero since it maynot involve editing. In another example, the operation score assigned toa given edit operation may be weighted by (e.g., multiplied by) apredefined weight associated with the character on which said editoperation is applied. Additionally, and in case the first type editoperation is immediately followed in the sequence by a second type editoperation, the score assignment rule assigns to the first type editoperation a switching score p (or penalty), p=SC, where SC is a value ofthe penalty. The switching score may, for example, be equal to one orany arbitrary number, preferably less than the sum of the cost forinsertion and removal of a character. The switching score may be named afirst type switching score in case the first type edit operation isimmediately followed in the sequence by a second type edit operation.The fact that the first type edit operation is immediately followed inthe sequence by the second type edit operation may be referred to as afirst switching type. The first type switching score provides a penaltyof changing the type of operations from the first type to the secondtype. Alternatively, or additionally, the score assignment rule mayassign to the first type edit operation the switching score p in casethe first type edit operation is immediately preceded in the sequence bya second type edit operation. The switching score may be named, in thiscase, second type switching score. The fact that the first type editoperation is immediately preceded in the sequence by the second typeedit operation may be referred to as a second switching type. The secondtype switching score may optionally be used. For that, differentimplementations of the application of the switching scores may be used.In one implementation example, the switching score may be defined asp=w_(sw)×SC, where w_(sw) may be set to a value that enables or disablesthe switching score. The first type switching score and second typeswitching score may be associated with weights w_(sw1) and W_(sw2)respectively, wherein the weight of the first type switching score maybe set to 1, w_(sw1)=1 as the switching score is enabled, while thesecond type switching score may be set to one if it is considered, orzero if not, e.g., w_(sw2)=1 or 0. The first type switching score may bep=w_(sw1)×SC and the second type switching score may be p=w_(sw2)×SC.Thus, the application of the score assignment rule on the sequence ofone or more edit operations may result in switching scores and/oroperation scores for the sequence of edit operations. The distancealgorithm may combine these switching scores and/or operation scoresassociated with the sequence of edit operations to obtain a combinedscore or edit distance that is indicative of the similarity levelbetween the first and second strings. The combination may, for example,be performed by summing the scores. The distance algorithm may beadvantageous as it may provide an accurate edit distance between thecompared strings. Using the combination of switching and operationscores, the score assignment rule according to the present subjectmatter may enable an accurate edit distance regardless of how thesequence of edit operations are determined. The sequence of editoperations may be obtained using different techniques. For example,different candidate sequences of edit operations may be determined andthe candidate sequence that provides the lowest combined score or lowestedit distance may be selected. For example, the string “shop” may beobtained from “soup” by four deletion operations for deleting characters“s”, “o”, “u”, “p”; and four insertion operations for inserting “s”,“h”, “o”, “p”, resulting in a sequence of 8 operations DDDDIIII whichwould give a distance of 8 since with 8 edit operations the two can beconverted and the operation score is 1. However, the minimal set ofoperations that can do this transformation may have a smaller distancee.g., “shop” may be obtained from “soup” by maintaining “s”, inserting“h”, maintaining “o”, removing “u” and maintaining “p” which is asequence of 5 operations MIMDM having a lower distance of 6 e.g., fourswitching scores and two operations scores, where the switching score is1 and the operation score is 1. In another example, the sequence of editoperations may be obtained using a known technique such as theLevenshtein edit distance technique.

In the following, the initial two strings to be compared may be referredto as string s₁ having N₁ characters, where N₁≥0 and string s₂ having N₂characters, where N₂≥0. The distance algorithm may be configured tocompute the combined score (distance) for two input strings of thealgorithm which are referred to as first string and second string,wherein the first string has n₁ characters and the second string has n₂characters. Depending on the way the distance algorithm is executed, n₁and n₂ may or may not be equal to N₁ and N₂ respectively, 0≤n₁≤N₁ and0≤n₂≤N₂. The combined score determined for n₁=0 and n₂>0 may indicatethe cost for converting an empty character to the n₂ characters whichmay correspond to n₂ insertion operations. The combined score determinedfor n₁>0 and n₂=0 may indicate the cost for converting the n₁ charactersinto an empty character which may correspond to n₁ removal operations.

In one first implementation example, the distance algorithm may beconfigured to compute at once the similarity between the first stringand the second string without relying on previously computed scorese.g., it may compute the combined score of the two strings independentof any other previously computed scores. In this case, for determiningthe similarity between the strings s₁ and s₂, the distance algorithm maybe called one time with one input pair (first string, second string).For example, the two strings s₁ and s₂ may be compared (at once) by thedistance algorithm by inputting the two strings s₁ and s₂ to thedistance algorithm (i.e., the first string is s₁ and the second stringis s₂) for obtaining the combined score.

According to one embodiment, in case n₁=N₁ and n₂=N₂ the obtainedcombined score is indicative of the distance between the string s₁ andstring s₂. The obtained combined score may be the edit distance betweenthe string s₁ and string s₂. For example, if the string s₁ comprises asequence of characters “sp” and the second string s₂ comprises thesequence of characters “shop”, the sequence of edit operations to beperformed on the characters “sp” in order obtain the sequence ofcharacters “shop” are, maintenance operation (because “s” ismaintained), two consecutive insertion operations to insert thecharacters “ho” and one maintenance operation to maintain “p”. Thesequence of operations in this case may be MIIM. It is to be noted thatother sequences of operations (I, M and D) may be determined using othertechniques. The distance algorithm may apply the score assignment ruleon the sequence MIIM. The first maintenance operation may receive anoperation score OS1=0 since it is an operation of the second type. Thesecond operation “I” may receive an operation score OS2=1 because it isof the first type. The second operation “I” may further receive thesecond type switching score w_(sw2)×SC because there was a switchingfrom the first operation which is of second type “M” to the secondoperation of first type “I”. The third operation “I” may receive anoperation score OS3=1 because it is of the first type. Moreover, thethird operation may receive the first type switching score w_(sw1)×SCbecause the operation that immediately follows “I” is “M”, an operationof the second type. The last operation may receive an operation scoreOS4=0 since it is an operation of the second type. The combined scoremay, for example, be obtained by summing the scores or using othercombination techniques e.g., the combined score may be equal to the sum:w_(sw2)×SC +OS1+OS2+OS3+w_(sw1)×SC+OS4, where w_(sw1)=1 and W_(sw2)=1.

According to one embodiment, the method further comprises providing acharacter weight to each character of the string s₁ and the string s₂,wherein the association of the operation score to the first type editoperation comprises weighting the operation score with the characterweight of the character involved in the first type edit operation,wherein the association of the first type switching score to the firsttype edit operation comprises weighting the switching score p with aweight w_(c), the weight w_(c) being a function of the combined score ofa subsequence of operations having as a last operation said first typeedit operation and the number of first type edit operations in saidsubsequence. For example, the function may be the ratio of the combinedscore and the number of first type edit operations, but it is notlimited to as other functions may be used. In this embodiment, thesecond type switching score may not be applied. Following the aboveexample of s₁=“sp” and s₂=“shop”, the combined score may be equal to:

w _(sw2) ×SC+w1×OS1+w2×OS2+w3×OS3+w _(c) ×w _(sw1) ×SC+w4×OS4,

wherein the weights w1 to w4 are the weights associated with the 4characters of the string s₂ respectively, w_(c) is the combined scoreassigned to the subsequence of operations including and preceding theedit operation that has been assigned the switching score w_(sw1)×SCdivided by the number of operations in said subsequence, w_(sw1)=1 andW_(sw2)=0. w_(c) may be referred to as average character weight. Indeed,since the penalty may depend on the weight of the characters to beinserted (or removed), a penalty may not be assigned upfront i.e.,w_(sw2)=0, when there is a deviation from diagonal processing (identicalstring) to non-diagonal progression. Instead, the penalty may beassigned i.e., w_(sw1)=1 when returning from a non-diagonal progressionto a diagonal progression. The diagonal and non-diagonal progressionsrefer to the iterative implementation using a matrix. The deviation fromthe diagonal to non-diagonal progressions refers to the second switchingtype and the deviation from the non-diagonal to diagonal progressionsrefers to the first switching type.

According to one embodiment, the distance algorithm is furtherconfigured for: associating to each first type edit operation of thesequence of edit operations the switching score if it is immediatelypreceded in the sequence by a second type edit operation. Following theabove example of s₁=“sp” and s₂=“shop”, the second operation “I” mayfurther be assigned a switching score w_(sw2)×SC according to thisembodiment because it is immediately preceded by the first operationwhich is of the second type. In this case, the combined score may bew_(sw2)×SC+OS1OS2+OS3+w_(sw1)×SC+OS4, where w_(sw1)=1 and w_(sw2)=1.This switching score w_(sw2)×SC may advantageously be used for scorescomputed without character weights.

According to one embodiment, N₁≥1 and/or N₂≥1. The similarity levelbetween string s₁ and s₂ may be modeled by the following function:

${s_{gl} = {1. - \frac{d_{gl}\left( {s_{1},s_{2}} \right)}{{❘s_{1}❘} + {❘s_{2}❘} + {p\overset{¯}{w}\min\left( {{2{❘s_{1}❘}},{{❘s_{2}❘} - 1}} \right)}}}},$

where w is an average character weight of the strings s₁ and s₂, whered_(gl)(s₁, s₂) is the combined score obtained by the present method, andp is switching score.

In one second implementation example that may reuse previouscomputations, the distance algorithm may use previously computedcombined scores if it is iteratively called for processing of thecharacters of the strings s₁ and s₂. In this case, for determining thedistance between the strings s₁ and s₂, the distance algorithm may becalled multiple times, wherein the result of the last iteration may bethe combined score that indicates the distance/similarity between theinitial strings s₁ and s₂. In this example, the distance algorithm maydetermine the sequence of operations for a current pair of the firststring and the second string based on sequences that have beenpreviously computed in previous iterations. This may save resources thatwould otherwise be required for unnecessary repeated determination ofoperations. In this case, the distance algorithm was first called withthe first string having n₁=0 characters of the string s₁ and the secondstring having n₂=0 characters of the string s₂, which correspond to twoempty characters. The distance algorithm may determine that the emptycharacters have a distance of zero. Using values of n₁ and n₂ that startfrom zero may be advantageous as it may enable to set initial values ofthe combined scores for pairs (first string, second string) havingvalues n₁=0 or n₂=0. Thus, according to one embodiment, the methodfurther comprises providing or determining or setting initial values ofthe combined scores for pairs of first and second strings havingrespectively n₁ and n₂ characters, wherein n₁=0 and n₂=0, 1, . . . N₂;or n₂=0 and n₁=0, 1, . . . N₁. This is shown in FIG. 5A-B where theinitial values may be provided for the first row and first column of thematrix.

Hence, according to one embodiment of the second implementation example,the first n₁ characters of the string s₁ and first n₂ characters of thestring s₂ may repeatedly be input to the distance algorithm, wherein newvalues of n₁ and n₂ are chosen in each iteration according to a nestedloop until n₁=N₁ and n₂=N₂ characters respectively, wherein n₁represents the outer loop and n₂ represents the inner loop. That is, fora given iteration, n₁ may be fixed to a value between 0 and N₁ and n₂may be incremented from 0 to N₂, then n₁ may be fixed to a next valuebetween 0 and N₁ and so on. This may enable to implement this secondimplementation example in a matrix as described in FIGS. 5A-B. In eachiteration, the distance algorithm may determine/check whether:

-   -   a first combined score has been previously determined (e.g., or        was set/initialized) for the first string having n₁−1 (and        n₁−1≥0) characters and the second string having n₂ characters        using a first sequence of the edit operations, and/or    -   a second combined score was previously determined (e.g., or was        set/initialized) for the first string having n₁ characters and        the second string having n₂−1 (and n₂−1≥0) characters using a        second sequence of the edit operations, and/or    -   a third combined score was previously determined (e.g., or was        set/initialized) for the first string having n₁−1 (and n₁−1≥0)        characters and the second string having n₂−1 (and n₂−1≥0)        characters using a third sequence of the edit operations, and        the last character of the first string and the second string        being the same.

If all the checked combined scores have been previously computed and/orset, the distance algorithm may then select the lowest score of thecomputed/set combined scores. That is, the selected lowest score may bethe first or second or third combined score. The selected lowest scoremay be the combined score determined for a selected sequence of editoperations, wherein the selected sequence of edit operations is thefirst, second or third sequence of edit operations whose combined scorewas selected as the lowest score. The distance algorithm may determinean additional operation to be performed in addition to the selectedsequence of edit operations in order to obtain the second string of thecurrent iteration from the first string of the current iteration.Therefore, the sequence of edit operations for converting the firststring to the second string of the present iteration comprises theselected sequence of edit operations plus the determined additionaloperation. The distance algorithm may apply the score assignment ruletaking into account the additional operation. This may result in anoperation score and to a switching score if the operation preceding theadditional operation in the sequence is of a different type. Theselected lowest score may be combined with the resulting operation scoreand switching score induced by the additional operation. This combinedscore is the edit distance between the first string and the secondstring of the present iteration.

If at least one of the checked combined scores has not been previouslycomputed or set, the distance algorithm may compute that at least onecombined score before selecting the lowest score and determining thesequence of operations and the edit distance as described above.However, this may only occur for pairs of first string second stringhaving n₁=0 or n₂=0 (e.g., n₁=0 and n₂=0 represent the empty characters)and no corresponding initial or set values are provided beforehand.

According to one embodiment, the method further comprises saving thecombined scores computed for each pair of the first string having n₁=N₁characters and second string having n₂ characters varying from 0 to N₂,and the combined scores computed for each pair of first string having n₁characters varying from 0 to N₁ and second string having n₂=N₂characters. The method further comprises receiving a request to comparetwo strings s₃ and s₄, wherein s₃=s₁+m₁ and s₄=s₂+m₂, wherein m₁ and m₂are strings of zero or more characters. The second exampleimplementation of the method may be applied on s₃ and s₄ using the savedscores, by repeatedly inputting the first n₁ characters of the string s₃and first n₂ characters of the string s₄ to the distance algorithm bychanging in each iteration n₁ and n₂ to new values (as described above),wherein the values of n₁ iterate over the range 0 . . . N₁ while thevalues of n₂ iterate over the range N₂+1 . . . N₄ (right quadrant of thematrix) and subsequently the values of n₁ iterate over the range N₁+1 .. . N₃ while the values of n₂ iterate over the range 0 . . . N₄ (lowertwo quadrants of the matrix).

According to one embodiment, the determining of the sequence ofoperations and the association of the operation scores and the switchingscores are performed character wise in parallel.

According to one embodiment, the distance algorithm may be used toperform record matching between two records. The record matchingcomprises comparing pairs of attributes values of the two records usingthe distance algorithm resulting in individual similarity levels of theattributes and combining the individual similarity levels fordetermining whether the two records are matching records. The distancealgorithm may be executed in accordance with any of the previouslydescribed example implementations.

A data record or record is a collection of related data items such as aname, date of birth and class of a particular user. A record representsan entity, wherein an entity refers to a user, object, or concept aboutwhich information is stored in the record. The terms “data record” and“record” are interchangeably used. The data records may, for example, bestored in a graph database as entities with relationships, where eachrecord may be assigned to a node or vertex of the graph with propertiesbeing attribute values such as name, date of birth etc. The data recordsmay, in another example, be records of a relational database.

Matching of records comprises comparing attribute values of the records.For example, if the records comprise a set of attributes a1 to an, thecomparison between two records is performed by comparing the n pairs ofvalues of the attributes a1 to an respectively. The comparison betweentwo or more records may thus result in n individual similarity levelsindicative of the level of similarity of the values of the respectiveattributes a1 to an. A level of similarity (or level of matching)between the compared records may be a combination (e.g., average) of theindividual levels of similarities. The level of matching of two recordsindicates the degree of similarity of the attribute values of the tworecords. Each similarity of the level of similarity, the individuallevel of similarities and the word level similarities may be provided asa normalized value (e.g., between 0 and 1) or any other format thatenables to match the records. If the level of matching is higher than apredefined similarity threshold, this indicates that the two records arematching. A deduplication system built on this disclosure may then mergethe records because they represent the same entity. The merging ofrecords is an operation which can be implemented in different ways. Forexample, the merging of two records may comprise creating a goldenrecord as a replacement of the similar looking records which have beenfound to be duplicates to each other. This is known as data fusion orphysical collapse with either record or attribute level survivorship. Ifthe level of matching is smaller than or equal to the predefinedsimilarity threshold, this indicates that the two records are notmatching and may thus be kept separate data records.

According to one embodiment, N₁≥1 and/or N₂≥1. The similarity levelbetween string s₁ and string s₂ may be modeled by the followingfunction:

${s_{gl} = {1. - \frac{d_{gl}\left( {s_{1},s_{2}} \right)}{{❘s_{1}❘} + {❘s_{2}❘} + {p\min\left( {{2{❘s_{1}❘}},{{❘s_{2}❘} - 1}} \right)}}}},$

where p is the switching score and d_(gl)(s₁, s₂) is the combined score.

FIG. 1 depicts an exemplary computer system 100. The computer system 100may, for example, be configured to perform master data management and/ordata warehousing e.g., the computer system 100 may enable ade-duplication system. The computer system 100 comprises a dataintegration system 101 and one or more client systems or data sources105. The client system 105 may comprise a computer system (e.g., asdescribed with reference to FIG. 8 ). The client systems 105 maycommunicate with the data integration system 101 via a networkconnection which comprises, for example, a wireless local area network(WLAN) connection, WAN (Wide Area Network) connection, LAN (Local AreaNetwork) connection the internet or a combination thereof. The dataintegration system 101 may control access (read and write accesses etc.)to a central repository 103.

Data records stored in the central repository 103 may have values of aset of attributes 109A-P such as a company name attribute. Although thepresent example is described in terms of few attributes, more or lessattributes may be used. The dataset 107 that is used in accordance withthe present subject matter may comprise at least part of the records ofthe central repository 103.

Data records stored in the central repository 103 may be received fromthe client systems 105 and processed by the data integration system 101before being stored in the central repository 103. The received recordsmay or may not have the same set of attributes 109A-P. For example, adata record received from client system 105 by the data integrationsystem 101 may not have all values of the set of attributes 109A-P e.g.the data record may have values of a subset of attributes of the set ofattributes 109A-P and may not have values for the remaining attributes.In other terms, the records provided by the client systems 105 may havedifferent completeness. The completeness is the ratio of number ofattributes of a data record comprising data values to a total number ofattributes in the set of attributes 109A-P. In addition, the receivedrecords from the client systems 105 may have a structure different fromthe structure of the stored records of the central repository 103. Forexample, a client system 105 may be configured to provide records in XMLformat, JSON format or other formats that enable to associate attributesand corresponding attribute values.

In another example, data integration system 101 may import data recordsof the central repository 103 from a client system 105 using one or moreExtract-Transform-Load (ETL) batch processes or via HyperText TransportProtocol (“HTTP”) communication or via other types of data exchange.

The data integration system 101 may, for example, be configured toprocess the received records e.g., to identify duplicate records. Forthat, a distance algorithm 120 implementing at least part of the presentmethod may be used. For example, the data integration system 101 mayprocess a data record received from the client systems 105 using thedistance algorithm 120 in order to find matching records in the dataset107.

FIG. 2 is a flowchart of a method for determining a similarity betweentwo strings in accordance with an example of the present subject matter.For the purpose of explanation, the method described in FIG. 2 may beimplemented in the system illustrated in FIG. 1 , but is not limited tothis implementation. The distance algorithm 120 may be configured toperform the method of FIG. 2 .

A first string and a second string may be received in step 201. Thefirst string comprises a sequence of n₁ characters and the second stringcomprises a sequence of n₂ characters.

A sequence of one or more edit operations to be performed on charactersof the first string in order to obtain the second string may bedetermined in step 203. The determination of the sequence of editoperations may be performed in different ways. For example, in case ofthe second implementation example of the distance algorithm, thesequence of edit operations may be determined as described withreference to FIG. 4 . The sequence of edit operations may be determinedusing previously computed scores e.g., as described with steps 403 to409 of FIG. 4 . This may particularly be advantageous in case thedistance algorithm is called iteratively to compute the distance. Incase of the first implementation example of the distance algorithm, thesequence of edit operations may be determined as described withreference to FIG. 3 . In another example, a known technique such as theLevenshtein edit distance technique may be used to determine thesequence of edit operations.

Each operation of the sequence of one or more edit operations may beassigned in step 205 an operation score and potentially an additionalswitching score depending on the type of the edit operation. Forexample, if the operation is a first type edit operation it may beassociated with an operation score indicative of a cost for applying theedit operation. In addition, if the operation is a first type editoperation which is immediately followed in the sequence by a second typeedit operation it may further be associated with a switching score. Incase of the iterative implementation of the distance algorithm, thepreviously assigned scores may be used in step 205 instead of assigninganew these scores to the previously processed edit operations. Forexample, if the sequence of edit operations for the current iteration is“DII”, the combined score obtained in a previous iteration for thesequence “DI” may be used to compute the combined score for “DII” inthis iteration.

The switching scores and/or operation scores associated with thesequence of edit operations may be combined in step 207. This may resultin a combined score that is indicative of the similarity level betweenthe first and second strings. The combined score may be an edit distancebetween the first string and the second string.

FIG. 3 is a flowchart of a method for determining a similarity betweentwo strings in accordance with an example of the present subject matter.For the purpose of explanation, the method described in FIG. 3 may beimplemented in the system illustrated in FIG. 1 , but is not limited tothis implementation.

Two strings s₁ and s₂ may be input of the distance algorithm in step301. The string s₁ of N₁ characters may be the first string of thedistance algorithm and the string s₂ of N₂ characters may be the secondstring of the distance algorithm.

The distance algorithm may determine, in step 303, the sequence of editoperations for obtaining the second string s₂ from the first string s₁.This may, for example, be performed by sequentially processing characterby character the first string s₁. The processing of each currentcharacter of the first string is performed by determining a currentsubsequence of characters of the first string s₁ that comprises thefirst x characters of the first string s₁ ending with the currentcharacter e.g., if the first string s₁ is “abcdef”, and the currentcharacter is “c”, then the determined current subsequence is “abc”.Furthermore, the operation(s) to be performed on the current subsequenceof characters of the first string s₁ in order to obtain a corresponding(same length) subsequence of the second string s₂ may be determined. Forexample, the second string “shop” may be obtained from the first string“soup” by first processing the first subsequence of characters “s” of“soup”. This would indicate that “s” is to be maintained as it is thesame as the corresponding subsequence “s” of the second string “shop”.The following subsequence of characters “so” associated with character“o” may be processed in order to determine operations in order to obtainthe corresponding subsequence “sh” from the second string “shop”. Thismay result in inserting “h”, resulting in edited first string “shoup”.The following subsequence of characters “shou” associated with character“u” may be processed in order to determine operations for obtaining thecorresponding subsequence “shop” of “shop”. This may result in deleting“u”, resulting in the edited first string “shop”. The followingsubsequence of characters “shop” associated with the last character “p”may be processed in order to determine operations for obtaining thecorresponding subsequence “shop” of the second string “shop”. This mayresult in maintaining “p”. The determined sequence of operations istherefore a sequence of 5 operations MIMDM.

The distance algorithm may apply in step 305 the score assignment ruleon each operation of the determined sequence of edit operations. Thismay result in each edit operation of the sequence of edit operationshaving an operation score and optionally an additional switching score.

The distance algorithm may compute in step 307 an edit distance betweenthe strings s₁ and s₂. The edit distance may, for example, be the sum ofall scores assigned to the determined sequence of edit operations.

In step 309, the edit distance between the strings s₁ and s₂ may bereceived e.g., as an output of the distance algorithm.

FIG. 4 is a flowchart of a method for determining a similarity betweentwo strings s₁ and s₂ in accordance with an example of the presentsubject matter. The string s₁ has N₁ characters and the string s₂ has N₂characters. For the purpose of explanation, the method described in FIG.4 may be implemented in the system illustrated in FIG. 1 , but is notlimited to this implementation.

The distance algorithm may receive in step 401 a first string having thefirst n₁ characters of the string s₁ and a second string having thefirst n₂ characters of the string s₂. Where 0≤n₁≤N₁ and 0≤n₂≤N₂. In thefirst execution of step 401, the distance algorithm may receive thefirst string having the first n₁=0 characters of the string s₁ and thesecond string having the first n₂=0 characters of the string s₂. Thatis, the distance algorithm may receive two empty characters.

The distance algorithm may check in step 403 whether it has determinedin a previous iteration or initialized a combined score for the pairs(named surrounding pairs) of first and second strings havingrespectively n′₁≥0 and n′₂≥0 characters, wherein the surrounding pairs(n′₁, n′₂) may comprise (n′₁=n₁, n′₂=n₂−1) and/or (n′₁=n₁−1, n′₂=n₂)pairs. The surrounding pairs (n′₁, n′₂) may further comprise (n′₁=n₁−1,n′₂=n₂−1) pair if the last character of the first string and the secondstring are the same. This condition may not be fulfilled only for thepairs (n′₁, n′₂) where n₁=0 or n₂=0. In case one or more pairs (missingpairs) of the surrounding pairs have not been previously processed orhave not been initialized with values, the distance algorithm maydetermine for each pair of the missing pairs in step 405 (from scratch)the one or more edit operations needed to obtain the n′₂ characters ofthe pair from the n′₁ characters of the pair. And a combined score maybe computed for the missing pairs. Then step 407 may be performed.

In case the distance algorithm has previously processed said surroundingpairs of n′₁ and n′₂ characters meaning that the distance algorithm haspreviously computed the edit distance for the surrounding pairs ofsequences of characters (n₁, n₂−1) and/or (n₁1, n₂) and/or (n₁−1, n₂−1)or has initialized said pairs with values, step 407 may be performed.

The distance algorithm may select in step 407 one of the surroundingpairs of sequences of characters (n₁, n₂−1) and/or (n₁−1, n₂) and/or(n₁−1, n₂−1) having the lowest edit distance. The distance algorithm mayhave determined in the previous iteration for the selected pair (n′₁,n′₂) a sequence of edit operations (named selected sequence of editoperations) to be performed on the first n′₁ characters of the string s₁in order to obtain the first n′₂ characters of the string s₂. Thus, thedistance algorithm may determine in step 409 or assume that the sequenceof edit operations to be performed on the first n₁ characters of thestring s₁ in order to obtain the first n₂ characters of the string s₂ issaid selected sequence of edit operations plus one additional editoperation. This one additional operation may depend on the selected pair(n′₁, n′₂). For example, if the selected pair is (n′₁, n′₂)=(n₁, n₂−1),then the one additional edit operation is an insertion operation. If theselected pair is (n′₁, n′₂)=(n₁−1, n₂), then the one additional editoperation is a removal operation. If the selected pair is (n′₁,n′₂)=(n₁−1, n₂−1), then the one additional edit operation is amaintenance operation.

The distance algorithm may determine in step 411 the edit distancebetween the first n₁ characters of the string s₁ and the first n₂characters of the string s₂ by applying the score assignment rule on theadditional edit operation and eventually on the last edit operation ofthe selected sequence of edit operations, resulting in an additionalscore. And, the sum of the additional score with the edit distancebetween the first n′₁ characters of the string s₁ and the first n′₂characters of the string s₂ may be provided as the edit distance betweenthe first n₁ characters of the string s₁ and the first n₂ characters ofthe string s₂.

It may be determined (step 413) whether n₁=N₁ AND n₂=N₂. If so, thecomputed edit distance in step 411 may be provided in step 415 as anedit distance between the strings s₁ and s₂. Otherwise, a new pair ofvalues of (n₁, n₂) may be defined in step 414 and steps 401 to 415 maybe repeated until n₁=N₁ and n₂=N₂ is reached. n₁ and n₂ may beincremented in each iteration according to a nested loop, where n₁represents the outer loop and n₂ represents the inner loop.

FIG. 5A is a flowchart of an example method for determining thesimilarity between the string s₁=“soup” having N₁=4 characters and thestring s₂=“shop” having N₂=4 characters using the second implementationexample. For that, the method of FIG. 5A may use a matrix whose firstdimension represents the characters of “soup” and whose second dimensionrepresents the characters of “shop”, but it is not limited to thismatrix implementation. The matrix implementation may enable an efficientusage of the processing resources. Indeed, the method fills a matrix rowby row so that instead of keeping the entire matrix one would only keepa single row in memory plus the updates to the current row. For example,if the first row of the matrix is currently stored in memory; the cellsof the second row may be consecutively computed until the second row iscompletely computed. Next, the second row is in the memory, and thethird row is filled and so on. Assuming, for example, that the operationscore and the switching score are equal to one.

In step 501, the distance algorithm may create a matrix M, 520A of size(N₁+1)×(N₂+1) as shown in FIG. 5B. The last N₂ columns of the matrixrepresent the N₂ characters of the string s₂. The last N₁ rows of thematrix represent the N₁ characters of the string s₁. The additionalfirst column and first row represent a special character ϵ representingthe empty string. The first row indicates the cost values for obtainingthe first n₂ characters of the string s₂ from an empty character e.g.,the cost to obtain “sho” from an empty character is 3 which correspondsto three insertion operations each having a cost value of 1. The firstcolumn indicates the cost values for obtaining an empty character ϵ fromthe first n₁ characters of the string s₁ e.g., the cost to obtain from“so” an empty character is 2 which corresponds to two removal operationseach having a cost value of 1. In other words, the matrix M isinitialized with initial cost values that may be used when comparing thestrings s₁ and s₂.

Each cell of the matrix M has two corresponding first string and secondstring. As shown in FIG. 5B, the cell M₂₂ has the pair of first andsecond strings (“s”, “s”), the cell M₂₃ has the pair of first and secondstrings (“s”, “sh”), the cell M₅₅ has the pair of first and secondstrings (“soup”, “shop”) etc. The second implementation example of thedistance algorithm may be performed by processing each cell of the cellse.g., M₂₂ to M₅₅ in one iteration in order to fill the cell with a costvalue. The cost value in each cell indicates the edit distance betweenthe first string and the second string associated with that cell.

The distance algorithm may execute steps 503 to 505 on each current cellM_(ij) (i is a row index and j is a column index) having a correspondingupper cell M_(i−1,j), left cell M_(i,j−1) and diagonal cell M_(i−1,j−1)(herein named surrounding cells) with a precomputed/initialized value(e.g., the surrounding cells may include the diagonal cell in case thecharacter assigned to the row i and column j is the same). For example,in the matrix 520A only the cell M₂₂ has those surrounding cells filledwith values and thus the distance algorithm may start with that cellM₂₂.

Hence, the distance algorithm may start with the cell M₂₂ having thepair of the first string and second string (“s”, “s”). The distancealgorithm may determine the cost for obtaining “s” from “s” which iszero since it involves a maintenance operation. This cost may be derivedfrom the three cell values surrounding the cell M₂₂ e.g., the distancealgorithm may determine that the upper cell value M₁₂ and the left cellvalue M₂₁ are equal to one and the diagonal cell value M₁₁ is zero. Thedistance algorithm may determine in step 503 the cost fortraveling/moving from M₁₁ to M₂₂, from M₁₂ to M₂₂ and from M₂₁ to M₂₂and select the lowest one. The cost for traveling from M₁₂ to M₂₂ isequal to the cost of cell M₁₂ plus the cost of the additional operationfor deleting the character “s” which is 1+1=2. The cost for travelingfrom M₂₁ to M₂₂ is equal to the cost of the cell M₂₁ plus the cost ofthe additional operation for inserting the character “s” which is:1+1=2. The cost for traveling from M₁₁ to M₂₂ is equal to the cost ofcell M₁₁ plus the cost of the additional operation for maintaining thecharacter “s” which is 0+0. Therefore, the distance algorithm may assignin step 505 to the cell M₂₂ the lowest value of zero. The value of cellM₂₂ is marked by an additional sign “+” in the resulting matrix 520B toindicate that the movement/traveling was along the diagonal to arrive atthis cell M₂₂ (i.e., the operation was a maintenance operation). Thematrix 520B comprises the resulting content after the first execution ofthe distance algorithm.

FIG. 5B shows the content of the matrix for different iterations of thedistance algorithm. For example, the matrix 520C represents the statusof the matrix before processing the current cell M₃₄ as indicated inFIG. 5B. As with cell M₂₂, the distance algorithm may determine that theupper cell value M₂₄ and the left cell value M₃₃ are equal to three andthe diagonal cell value M₂₃ is two. The distance algorithm may determinein step 503 the cost for traveling from M₂₃ to M₃₄, from M₂₄ to M₃₄ andfrom M₃₃ to M₃₄ and select the lowest one. The cost for traveling fromM₂₄ to M₃₄ is equal to the cost of the cell M₂₄ plus the cost of theadditional operation for deleting the character “o” which is 3+1=4. Thecost for traveling from M₃₃ to M₃₄ is equal to the cost of the cell M₃₃plus the cost of the additional operation for inserting the character“o” which is: 3+1=4. The cost for traveling from M₂₃ to M₃₄ is equal tothe cost of the cell M₂₃ plus the cost zero of the additional operationfor maintaining the character “o” and the switching score 1 forswitching to the maintenance operation, thus 2+1. Therefore, thedistance algorithm may assign in step 505 to the cell M₃₄ the lowestvalue of 3. Since the value of M₃₄ is obtained from the correspondingdiagonal cell it is marked by an additional sign “+” in the resultingmatrix 520D. FIG. 5B shows the content of the matrix M 520E after thefinal iteration of the distance algorithm. In one example, the last rowand last column of the matrix 520E may be saved so that they can bereused in case of comparing two strings comprising “soup” and “shop”respectively. For example, for computing the edit distance between thetwo strings “ϵsouppap” and “ϵshopping” having N₃ and N₄ charactersrespectively, a new N₃×N₄ matrix may be used, wherein only the cells ofthe last 4 columns and last 3 rows of the new matrix representing thetwo strings may be computed because the saved row and column may beused. This may, for example, be performed by repeatedly inputting thefirst n₁ characters of the string “ϵsouppap” and first n₂ characters ofthe string “ϵshopping” to the distance algorithm by changing in eachiteration n₁ and n₂ to new values (as described above), wherein thevalues of n₁ iterate over the range 0 . . . N₁ while the values of n₂iterate over the range N₂+1 . . . N₄ (right quadrant of the new matrix)and subsequently the values of n₁ iterate over the range N₁+1 . . . N₃while the values of n₂ iterate over the range 0 . . . N₄ (lower twoquadrants of the new matrix).

The distance algorithm may provide in step 507 the value of the bottomright cell M₅₅ as an edit distance between the string s₁=“soup” and thestring s₂=“shop”.

The present method of FIG. 5A may favor words where the edit operationsare in the same location. Another way of putting this is favoring wordsthat have a longer stretch of identical letters, i.e., in the matrix M alonger series were the computation progresses along the diagonal. Thereare between 2^(|s1|+|s2|−1) and 3^(|s1|+|s2|−1) different paths to getfrom the top left to the bottom right (allowing character replacementwould always give 3^(|s1|+|s2|−1) possibilities). Hence, computing allthe paths and finding the one with the longest running diagonal is NPcomplete. The present method, instead, may rely on adding a penalty towhenever there is a change to or from a diagonal progression through thematrix while building the matrix.

In another example, using the method of FIG. 5A for comparing the strings₁=“shop” and the string s₂=“shopping”, the matrix 520F may be obtained.The matrix 520F gives a distance of 5 between s₁ and s₂ (essentially,progressing along the diagonal for “shop” as indicated in FIG. 5B addinga penalty for the deviation, and the 4 insertion operations of “p”, “i”,“n”, “g”). Since the computed distance between s₁ and s₂ may have avalue higher than the sum of the lengths of the two strings (s₁+|s2|),the present method may avoid this by adopting the following. If bothstrings are empty, the similarity is 1. If not, it may be assumedwithout loss of generality that s₁ is the shorter string. Then at most2|s₁| penalties may be introduced, if all characters in s₁ are containedin s₂ and at most |s₂|−1 penalties if |s₂≤2|s₁|, i.e., there are notenough characters to add two penalties for each character in s1.

FIG. 6A is a flowchart of an example method for determining thesimilarity between the string s₁=“Dun” having N₁=4 characters and thestring s₂=“Du{umlaut over ( )}rr” having N₂=5 characters using thesecond implementation example. As with FIG. 5A, the method of FIG. 6Amay use a matrix whose first dimension represents the characters of“Durr” and whose second dimension represents the characters of “37Du{umlaut over ( )}rr”, but it is not limited to this matriximplementation. Assuming, in this example, that the operation score andthe first type switching score are equal to one (the second typeswitching score may not be used in this example as this example involvescharacter weights). The first type switching score may be weighted withthe average character weight. In addition, each character of the stringss₁ and s₂ may be associated with a respective weight. This is indicatedin FIG. 6B, where each of the characters is associated with a weight 10,except the character “{umlaut over ( )}” which is associated with aweight 1. However, in order to be able to assign the penalty in thisimplementation using the matrix, one record for each element in thematrix the number of insertion and removal operations that had beenassigned as well as the combined score. This is indicated for each cellof the matrix by the pair [cost/len] where “cost” represents thecombined score and “len” is the number of insertion and/or removaloperations accrued so far. For example, the cell M₄₂ of the matrix 620Ais associated with the pair [20/2] indicating the number of operationsis 2 and that the combined score computed for the first string “Dur” andsecond string “D” by the distance algorithm is 20. The number of firsttype edit operations is 2 delete operations because the set ofoperations for obtaining “D” from “Dur” comprises one maintenanceoperation to maintain “D” and two delete operations to delete “u” and“r”. The penalty may be calculated by dividing the total penalty “cost”by the number “len” of characters (to get the average character weight)and multiply this by a constant p=w_(sw1)×SC. When the penalty isassigned, the number “len” of insertion and/or removal operations andthe penalty “cost” accumulated so far is reset to zero because thecorresponding penalty has already been integrated in the costaccumulated so far.

In step 601, the distance algorithm may create a matrix M, 620A of size(N₁+1)×(N₂+1) as shown in FIG. 6B. The last N₂ columns of the matrixrepresent the N₂ characters of the string s₂. The last N₁ rows of thematrix represent the N₁ characters of the string s₁. The additionalfirst column and first row represent a special character representingthe empty string. The first row indicates the cost values for obtainingthe first n₂ characters of the string s₂ from an empty character e.g.,the cost to obtain “Du{umlaut over ( )}” from an empty character is 21which corresponds to three insertion operations each having a cost valueof 1 and having weights 10, 10 and 1 respectively i.e., 10*1+10*1+1*1.The first column indicates the cost values for obtaining an emptycharacter from the first n₁ characters of the string s₁ e.g., the costto obtain from “Du” an empty character is 20 which corresponds to tworemoval operations each having a cost value of 1 and a weight of 10i.e., 10*1 +10*1. In other words, the matrix is initialized with initialcost values that may be used when comparing the strings s₁ and s₂.

Each cell of the matrix M has two corresponding first string and secondstring. As shown in FIG. 6B, the cell M₂₂ has the pair of first andsecond strings (“D”, “D”), the cell M₂₃ has the pair of first and secondstrings (“D”, “Du”), the cell M₅₆ has the pair of first and secondstrings (“Dun”, “37 Du{umlaut over ( )}rr”) etc. The implementation ofthe distance algorithm may be performed by processing each cell of thecells e.g., M₂₂ to M₅₆ in one iteration in order to fill the cell with acost value. The cost value in each cell indicates the edit distancebetween the first string and the second string associated with thatcell.

The distance algorithm may execute steps 603 to 605 on each current cellM_(ij) (i is a row index and j is a column index) having thecorresponding surrounding cells M_(i−1, j), M_(i, j−1) and M_(i−1, j−1)with a precomputed/initialized value e.g., the surrounding cells mayinclude the diagonal cell M_(i−1, j−1) in case the character assigned tothe row i and column j is the same. For example, FIG. 6B shows thestatus of the matrix 620A after multiple iterations of the steps 603 to605. In matrix 620 A the distance algorithm may process the cell M₄₅ inthe next iteration of step 603 to 605, as it operates on a row by rowbasis.

For a current cell M_(ij), the distance algorithm may determine in step603 the cost for traveling from M_(i−1, j−1) to M_(ij), from M_(i−1, j)to M_(ij) and from M_(i, j−1) to M_(ij) and select the lowest one. Thecost for traveling from M_(i−1, j−1) to M_(ij) may bedetermined/considered in case the character assigned to the row i andcolumn j is the same. The cost for traveling from M_(i−1, j) to M_(ij)is equal to the cost of the cell M_(i−1, j) plus the cost of theadditional operation for deleting the character assigned to the row i.The cost for traveling from M_(i, j−1) to M_(ij) is equal to the cost ofthe cell M_(i, j−1) plus the cost of the additional operation forinserting the character assigned to assigned to the column j. The costfor traveling from M_(i−1, j−1) to M_(ij) is equal to the cost of thecell M_(i−1, j−1) plus the cost induced by the additional operation formaintaining the same character assigned to the row i and column j.Therefore, the distance algorithm may assign in step 605 to the cellM_(ij) the lowest cost value of the determined cost values. If forexample, the lowest traveling cost is from M_(i−1, j−1) to M_(ij), thecost induced by the additional operation may be the first type switchingscore w_(sw1)×SC weighted by the average character weight

${w_{c} = \frac{cost}{len}},$

where [cost, len] are the recorded cost and number for the cellM_(i−1, j−1). That is, the combined score for cell M_(ij) may be equalto: cost+w_(c)×w_(sw1)×SC.

For example, with the content of matrix 620A of FIG. 6B, the distancealgorithm may process the cell M₄₅ by determining in step 603 the costfor traveling from M₃₄ to M₄₅, from M₃₅ to M₄₅ and from M₄₄ to M₄₅ andselect the lowest one. The cost for traveling from M₃₅ to M₄₅ is equalto the cost of the cell M₃₅ plus the cost of the additional operationfor deleting the character “r” assigned to the row 4. The cost fortraveling from M₄₄ to M₄₅ is equal to the cost of the cell M₄₄ plus thecost of the additional operation for inserting the character “r”assigned to assigned to the column 5. The cost for traveling fromM_(3,4) to M₄₅ is equal to the cost of the cell M₃₄ plus the costinduced by the additional operation for maintaining the same character“r” assigned to the row 4 and column 5, wherein the cost induced by theadditional operation includes the first type switching score w_(sw1)×SCbecause the additional operation is of the second type and the lastoperation is of the first type, wherein the first type switching scoreis weighted with the average character weight associated with the cellM₃₄, i.e., w_(c)=cost/len=1/1=1. Therefore, the distance algorithm mayassign in step 505 to the cell M₄₅ the lowest cost value 2.

The content of the matrix 520B is the resulting content after processingall cells of the matrix. The distance algorithm may provide in step 607the value of the bottom right cell M₅₆ as an edit distance between thestring s₁=“Durr” and the string s₂=“Du{umlaut over ( )}rr”. FIG. 6Bshows another matrix 620C that is the result of performing the iterativeimplementation of the distance algorithm between the string s₁=“Durst”and the string s₂=“Du{umlaut over ( )}rr”.

In one example, multiple similarity metrics s1, s2, . . . , sn may becombined. For that the following formula may be used: sc=0.9 max(s1, . .. , sn)+0.1 min(s1, . . . , sn). With this approach, differentsimilarity metrics can capture different aspects of the similaritybetween two strings. For example, the Levenshtein function may capturethe edit distance, whereas the Jaccard similarity can deal with wordpermutations. On the other hand, the Jaccard similarity function canreturn a similarity of 1.0 for strings that differ which may not bedesirable. Hence, instead of simply using the maximum, one combine thefunction by combining the maximum and the minimum of the functions. Theuse of the approach presented in this disclosure may led to an increaseof 7% of recall (85 to 92%).

FIG. 7 shows in pseudo-code the elements of an example workflow of thematrix-based string comparison method for comparing strings s₁ and s₂.It assumes that the strings have been preprocessed and the initialvalues associated with the empty string element (i.e., the values of thefirst column and the first row of the matrix) has already been added tothe string in a suitable way. It also uses the two penalty functionspen( ) and pend( ) The pseudo code uses a matrix implementation toobtain the edit distance between the strings s₁ and s₂. “(\e) row”refers to the empty row e.g., such as the first row of matrix 520A.“prey” refers to previous character. “cur” refers to current row of thematrix. “top” refers to the top row. “ch1” and “ch2” refer to thecharacters of the strings s₁ and s₂ respectively, “ch1” and “ch2 areassigned to the row and column of the current cell respectively.

“s2_dist” and “s2_pen” are the distance and penalty for a current cellusing the left cell, i.e., the cell on the same row but on the leftcolumn of the current cell. lft_dist refers to the distance assigned tothe left cell. lft_pen refers to the penalty assigned to the left cell.

“s1_dist” and “s1_pen” are the distance and penalty for the current cellusing the top cell, i.e., the cell on the same column but on the top rowof the current cell. top_dist refers to the distance assigned to the topcell. top_pen refers to the penalty assigned to the top cell.

“d2_dist” and “d2_pen” are the distance and penalty for the current cellusing the diagonal cell i.e., the cell on the left column and on the toprow of the current cell. d_dist refers to the distance assigned to thediagonal cell. d_pen refers to the penalty assigned to the diagonalcell.

The penalty refers to the switching score described herein and thedistance refers to the operation scores.

The function pen( ) returns the penalty if the penalty object passed toit has a length of zero representing the transition from a diagonalmovement to a lateral movement (representing the second switching type).Depending on whether the weighted version is implemented or not, thismay return zero or some penalty to be assigned. The function pend( )returns the average penalty captured by the penalty object representingthe transition from a lateral movement to a diagonal movement(representing the first switching type). Depending on whether theweighted version is implemented or not, this returns the accumulateddistance in the penalty object divided by the characters in the penaltyobject. In the weighted version of the code, depending on the penaltythat can be accrued, it may be beneficial to force following thediagonal line by changing the condition “ch1==ch2 && d2 dist<s1 dist &&&& d2_dist<s2_dist”. by the condition “ch1==ch2”.

FIG. 8 represents a general computerized system 800 (e.g., the dataintegration system) suited for implementing at least part of methodsteps as involved in the disclosure.

It will be appreciated that the methods described herein are at leastpartly non-interactive, and automated by way of computerized systems,such as servers or embedded systems. In exemplary embodiments though,the methods described herein can be implemented in a (partly)interactive system. These methods can further be implemented in software812, 822 (including firmware 822), hardware (processor) 805, or acombination thereof. In exemplary embodiments, the methods describedherein are implemented in software, as an executable program, and isexecuted by a special or general-purpose digital computer, such as apersonal computer, workstation, minicomputer, or mainframe computer. Themost general system 800 therefore includes a general-purpose computer801.

In exemplary embodiments, in terms of hardware architecture, as shown inFIG. 8 , the computer 801 includes a 805, memory (main memory) 810coupled to a memory controller 815, and one or more input and/or output(I/O) devices (or peripherals) 10, 845 that are communicatively coupledvia a local input/output controller 835. The input/output controller 835can be, but is not limited to, one or more buses or other wired orwireless connections, as is known in the art. The input/outputcontroller 835 may have additional elements, which are omitted forsimplicity, such as controllers, buffers (caches), drivers, repeaters,and receivers, to enable communications. Further, the local interfacemay include address, control, and/or data connections to enableappropriate communications among the aforementioned components. Asdescribed herein the I/O devices 10, 845 may generally include anygeneralized cryptographic card or smart card known in the art.

The processor 805 is a hardware device for executing software,particularly that stored in memory 810. The processor 805 can be anycustom made or commercially available processor, a central processingunit (CPU), an auxiliary processor among several processors associatedwith the computer 801, a semiconductor based microprocessor (in the formof a microchip or chip set), a macroprocessor, or generally any devicefor executing software instructions.

The memory 810 can include any one or combination of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmableread only memory (EPROM), electronically erasable programmable read onlymemory (EEPROM), programmable read only memory (PROM). Note that thememory 810 can have a distributed architecture, where various componentsare situated remote from one another, but can be accessed by theprocessor 805.

The software in 810 may include one or more separate programs, each ofwhich comprises an ordered listing of executable instructions forimplementing logical functions, notably functions involved inembodiments of this disclosure. In the example of FIG. 8 , software inthe memory 810 includes instructions 812 e.g. instructions to managedatabases such as a database management system.

The software in memory 810 shall also typically include a suitableoperating system (OS) 811. The OS 811 essentially controls the executionof other computer programs, such as possibly software 812 forimplementing methods as described herein.

The methods described herein may be in the form of a source program 812,executable program 812 (object code), script, or any other entitycomprising a set of instructions 812 to be performed. When a sourceprogram, then the program needs to be translated via a compiler,assembler, interpreter, or the like, which may or may not be includedwithin the memory 810, so as to operate properly in connection with theOS 811. Furthermore, the methods can be written as an object-orientedprogramming language, which has classes of data and methods, or aprocedure programming language, which has routines, subroutines, and/orfunctions.

In exemplary embodiments, a conventional 850 and mouse 855 can becoupled to the input/output controller 835. Other output devices such asthe I/O devices 845 may include input devices, for example but notlimited to a printer, a scanner, microphone, and the like. Finally, theI/O devices 10, 845 may further include devices that communicate bothinputs and outputs, for instance but not limited to, a network interfacecard (NIC) or modulator/demodulator (for accessing other files, devices,systems, or a network), a radio frequency (RF) or other transceiver, atelephonic interface, a bridge, a router, and the like. The I/O devices10, 845 can be any generalized cryptographic card or smart card known inthe art. The system 800 can further include a display controller 825coupled to a display 830. In exemplary embodiments, the system 800 canfurther include a network interface for coupling to a network 865. Thenetwork 865 can be an IP-based network for communication between thecomputer 801 and any external server, client and the like via abroadband connection. The network 865 transmits and receives databetween the computer 801 and external systems 30, which can be involvedto perform part, or all of the steps of the methods discussed herein. Inexemplary embodiments, network 865 can be a managed IP networkadministered by a service provider. The network 865 may be implementedin a wireless fashion, e.g., using wireless protocols and technologies,such as WiFi, WiMax, etc. The network 865 can also be a packet-switchednetwork such as a local area network, wide area network, metropolitanarea network, Internet network, or other similar type of networkenvironment. The network 865 may be a fixed wireless network, a wirelesslocal area network (LAN), a wireless wide area network (WAN) a personalarea network (PAN), a virtual private network (VPN), intranet or othersuitable network system and includes equipment for receiving andtransmitting signals.

If the computer 801 is a PC, workstation, intelligent device or thelike, the software in the memory 810 may further include a basic inputoutput system (BIOS) 822. The BIOS is a set of essential softwareroutines that initialize and test hardware at startup, start the OS 811,and support the transfer of data among the hardware devices. The BIOS isstored in ROM so that the BIOS can be executed when the computer 801 isactivated.

When the computer 801 is in operation, the processor 805 is configuredto execute software 812 stored within the memory 810, to communicatedata to and from the memory 810, and to generally control operations ofthe computer 801 pursuant to the software. The methods described hereinand the OS 811, in whole or in part, but typically the latter, are readby the processor 805, possibly buffered within the processor 805, andthen executed.

When the systems and methods described herein are implemented insoftware 812, as is shown in FIG. 8 , the methods can be stored on anycomputer readable medium, such as storage 820, for use by or inconnection with any computer related system or method. The storage 820may comprise a disk storage such as HDD storage.

The present subject matter may provide the following clauses.

Clause 1. A method for determining a distance between a string s₁ havingN₁ characters, where N₁≥0 and a string s₂ having N₂ characters, whereN₂≥0, the method comprising:

-   -   a. providing a distance algorithm being configured for:        -   i. receiving a first string and a second string;        -   ii. determining a sequence of one or more edit operations to            be performed on characters of the first string in order to            obtain the second string, the edit operation being of a            first type or a second type, the first type edit operation            comprising a character insertion operation or character            removal operation, the second type edit operation comprising            a character maintenance operation; wherein the first type            edit operation is associated with an operation score            indicative of a cost for applying the edit operation;            wherein the first type edit operation is associated with a            switching score indicative whether it is immediately            followed in the sequence by a second type edit operation;        -   iii. combining the switching scores and/or operation scores            associated with the sequence of edit operations, resulting            in a combined score that is indicative of the similarity            level between the first and second strings;    -   b. inputting first n₁ characters of the string s₁ as the first        string and first n₂ characters of the string s₂ as the second        string to the distance algorithm for obtaining the combined        score, wherein 0≤n₁≤N₁ and 0≤n₂≤N₂;    -   c. determining of the distance between the string s₁ and string        s₂ using the obtained combined score.

Clause 2. The method of clause 1, wherein in case n₁=N₁ and n₂=N₂, theobtained combined score is indicative of the distance between the strings₁ and string s₂.

Clause 3. The method of clause 1, wherein n₁=0 and n₂=0;

the inputting further comprising:

-   -   repeatedly inputting the first n₁ characters of the string s₁        and first n₂ characters of the string s₂ to the distance        algorithm, wherein n₁ and n₂ are incremented according to a        nested loop, where n₁ represents the outer loop and n₂        represents the inner loop;    -   wherein the distance algorithm is configured for determining the        sequence of edit operations in each iteration by:        -   determining whether:            -   a first combined score has been previously determined                for the first string having n₁−1 characters and the                second string having n₂ characters using a first                sequence of the edit operations, and/or            -   a second combined score was previously determined for                the first string having n₁ characters and the second                string having n₂−1 characters using a second sequence of                the edit operations, and/or            -   a third combined score was previously determined for the                first string having n₁−1 characters and the second                string having n₂−1 characters using a third sequence of                the edit operations, the last character of the first                string and the second string being the same;        -   determining a combined score of the first, second and third            combined scores if it was not previously determined and            selecting the lowest score of the determined combined            scores;        -   determining an additional operation to be performed in            addition to one of the first, second or third sequence of            edit operations associated with the selected lowest score in            order to obtain the second string from the first string,            wherein if the selected pair is (n₁, n₂−1), the additional            operation is the insertion operation, and if the selected            pair is (n₁−1, n₂), the additional operation is the removal            operation and if the selected pair is (n₁−1, n₂−1), the            additional operation is the maintenance operation.        -   wherein the sequence of edit operations comprises the one of            the first, second or third sequence of edit operations            associated with the selected lowest score and the determined            additional operation;    -   wherein the distance algorithm is configured in each iteration        for combining the switching scores and/or the operation scores        associated with the sequence of edit operations by combining the        lowest score with the switching score and/or the operation score        associated with the additional operation.

wherein the determining of the distance between the string s₁ and strings₂ is performed using the obtained combined score of the last iteration.

Clause 4. The method of clause 3, further comprising providing initialvalues of the combined scores for pairs of first and second stringshaving respectively n₁ and n₂ characters, wherein n₁=0 and n₂=0,1, . . .N₂ ; or n₂=0 and n₁=0, 1, . . . N₁.

Clause 5. The method of clause 3 or 4, further comprising:

-   -   saving the combined scores computed for each pair of first        string having n₁=N₁ characters and second string having n₂        varying from 1 to N₂ characters, and the combined scores        computed for each pair of first string having n₁ varying from 1        to N₁ characters and second string having n₂=N₂ characters;    -   receiving a request to compare two strings s₃ and s₄ having N₃        and N₄ characters respectively, wherein s₃=s₁+m₁ and s₄=s₂+m₂,        wherein m₁ and m₂ are strings of zero or more characters;    -   repeating the method using the saved scores, by repeatedly        inputting the first n₁ characters of the string s₃ and first n₂        characters of the string s₄ to the distance algorithm, wherein        the values of n₁ iterate over the range 0 . . . N₁ while the        values of n₂ iterate over the range N₂+1 . . . N₄ and        subsequently the values of n₁ iterate over the range N₁+1 . . .        N₃ while the values of n₂ iterate over the range 0 . . . N₄.

Clause 6. The method of any of the preceding clauses 1 to 6, furthercomprising providing a character weight to each character of the stringss₁ and s₂, wherein the association of the operation score to the firsttype edit operation comprises weighting the association score with thecharacter weight of the character involved in the first type editoperation.

Clause 7. The method of any of the preceding clauses 1 to 6, furthercomprising providing a character weight to each character of the strings₁ and the string s₂, wherein the association of the operation score tothe first type edit operation comprises weighting the operation scorewith the character weight of the character involved in the first typeedit operation, wherein the association of the switching score to thefirst type edit operation comprises weighting the switching score with aweight w_(c), the weight w_(c) being a predefined function of thecombined score of a subsequence of operations having as last operationsaid first type edit operation and the number of first type editoperations in said subsequence.

Clause 8. The method of clause 7, the function being a ratio of thecombined score and the number of first type edit operations in saidsubsequence.

Clause 9. The method of any of the preceding clauses 1 to 8, theswitching score being referred to as first type switching score, thedistance algorithm being further configured for: associating to eachfirst type edit operation of the sequence of edit operations a secondtype switching score if it is immediately preceded in the sequence by asecond type edit operation.

Clause 10. The method of any of the preceding clauses 1 to 9, whereinthe determining of the sequence of operations and the association of theoperation scores and the deviation scores are performed character wisein parallel.

Clause 11. The method of any of the preceding clauses 1 to 5, whereinN₁≥1 and N₂≥1, wherein the distance is converted into a similaritymeasure according to the following formula:

${s_{gl} = {1. - \frac{d_{gl}\left( {s_{1},s_{2}} \right)}{{❘s_{1}❘} + {❘s_{2}❘} + {p\min\left( {{2{❘s_{1}❘}},{{❘s_{2}❘} - 1}} \right)}}}},$

where p is the switching score, and d_(gl)(s₁, s₂) is the combinedscore.

Clause 12. The method of any of the preceding clauses 1 to 11, whereinthe string s₁ is shorter than string s₂.

Clause 13. The method of any of the preceding clauses 6 to 10, whereinN₁≥1 and N₂≥1, wherein the similarity level is further determinedaccording to the following distance:

${s_{gl} = {1. - \frac{d_{gl}\left( {s_{1},s_{2}} \right)}{{❘s_{1}❘} + {❘s_{2}❘} + {p\overset{\_}{w}\min\left( {{2{❘s_{1}❘}},{{❘s_{2}❘} - 1}} \right)}}}},$

where w is an average character weight of the strings s1 and s2, whered_(gl)(s₁, s₂) is the combined score and p is the switching score.

Clause 14. The method of any of the preceding clauses 1 to 13, theswitching score being smaller than the sum of the operation scores forone character insertion operation and one character removal operation.

Clause 15. The method of any of the preceding clauses 1 to 14, thedistance algorithm being configured to determine the sequence of one ormore edit operations by determining different candidate sequences ofedit operations and selecting the candidate sequence that provides thelowest combined score.

A method for determining a distance between a string s₁ having N₁characters, where N₁≥1 and a string s₂ having N₂ characters, where N₂≥1is provided. The method comprises:

-   -   a. providing a distance algorithm being configured for:        -   i. receiving a first string and a second string;        -   ii. determining a sequence of one or more edit operations to            be performed on characters of the first string in order to            obtain the second string, the edit operation being of a            first type or a second type, the first type edit operation            comprising a character insertion operation or character            removal operation, the second type edit operation comprising            a character maintenance operation; wherein the first type            edit operation is associated with an operation score            indicative of a cost for applying the edit operation;            wherein the first type edit operation is associated with a            switching score indicative whether it is immediately            followed in the sequence by a second type edit operation;        -   iii. combining the switching scores and/or operation scores            associated with the sequence of edit operations, resulting            in a combined score that is indicative of the similarity            level between the first and second strings;    -   b. inputting first n₁ characters of the string s₁ as the first        string and first n₂ characters of the string s₂ as the second        string to the distance algorithm for obtaining the combined        score, wherein 1≤n₁≤N₁ and 1≤n₂≤N₂;    -   c. determining of the distance between the string s₁ and string        s₂ using the obtained combined score.

The present disclosure may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

1. A method for programmatically determining a distance between a strings₁ having N₁ characters, where N₁≥0 and a string s₂ having N₂characters, where N₂≥0, the method comprising: providing, to a computersystem, a distance algorithm configured to be executed by the computingsystem, to: receive a first string and a second string; determine asequence of one or more edit operations to be performed on characters ofthe first string in order to obtain the second string, the editoperation being of a first type and/or a second type, the first typeedit operation comprising a character insertion operation and/orcharacter removal operation, the second type edit operation comprising acharacter maintenance operation; wherein the first type edit operationis associated with an operation score indicative of a cost for applyingthe edit operation; wherein the first type edit operation is associatedwith a switching score indicative whether it is immediately followed inthe sequence by a second type edit operation; combining the switchingscores and/or operation scores associated with the sequence of editoperations, resulting in a combined score that is indicative of thesimilarity level between the first and second strings; inputting firstn₁ characters of the string s₁ as the first string and first n₂characters of the string s₂ as the second string to the distancealgorithm for obtaining the combined score, wherein 0≤n₁≤N₁ and 0≤n₂≤N₂;repeatedly inputting the first n₁ characters of the string s₁ and firstn₂ characters of the string s₂ to the distance algorithm, wherein n₁ andn₂ are incremented according to a nested loop, where n₁ represents theouter loop and n₂ represents the inner loop; wherein the distancealgorithm is configured for determining the sequence of edit operationsin each iteration by: determining whether: a first combined score hasbeen previously determined for the first string having n₁−1 charactersand the second string having n₂ characters using a first sequence of theedit operations, and/or a second combined score was previouslydetermined for the first string having n₁ characters and the secondstring having n₂−1 characters using a second sequence of the editoperations, and/or a third combined score was previously determined,using a third sequence of the edit operations, for the first stringhaving n₁−1 characters and the second string having n₂−1 characters, andthe last character of the first string and the second string being thesame; determining a combined score of the first, second and thirdcombined scores if it is determined that it was not previouslydetermined and selecting the lowest score of the determined combinedscores; determining an additional operation to be performed in additionto one of the first, second or third sequence of edit operationsassociated with the selected lowest score in order to obtain the secondstring from the first string, wherein if the selected pair is (n₁,n₂−1), the additional operation is the insertion operation, and if theselected pair is (n₁−1, n₂), the additional operation is the removaloperation and if the selected pair is (n₁−1, n₂−1), the additionaloperation is the maintenance operation; wherein the sequence of editoperations comprises the one of the first, second or third sequence ofedit operations associated with the selected lowest score and thedetermined additional operation; wherein the distance algorithm isconfigured in each iteration for combining the switching scores and/orthe operation scores associated with the sequence of edit operations bycombining the lowest score with the switching score and/or the operationscore associated with the additional operation; and wherein thedetermining of the distance between the string s₁ and string s₂ isperformed using the obtained combined score of the last iteration.executing by the computer system the distance algorithm to determine thedistance between the first string and the second string using thecombined score; and implementing at least one action by the computersystem based upon the determined distance.
 2. The method of claim 1,wherein in case n₁=N₁ and n₂=N₂, the obtained combined score isindicative of the distance between the string s₁ and string s₂. 3.(canceled)
 4. The method of claim 1, further comprising providinginitial values of the combined scores for pairs of first and secondstrings having respectively n₁ and n₂ characters, wherein n₁=0 and n₂=0,1, . . . N₂; or n₂=0 and n₁=0, 1, . . . N₁.
 5. The method of claim 1,further comprising: saving the combined scores computed for each pair offirst string having n₁=N₁ characters and second string having n₂ varyingfrom 0 to N₂ characters, and the combined scores computed for each pairof first string having n₁ varying from 0 to N₁ characters and secondstring having n₂=N₂ characters; receiving a request to compare twostrings s₃ and s₄ having N₃ and N₄ characters respectively, whereins₃=s₁+m₁ and s₄=s₂+m₂, wherein m₁ and m₂ are strings of zero or morecharacters; and repeating the method using the saved scores, byrepeatedly inputting the first n₁ characters of the string s₃ and firstn₂ characters of the string s₄ to the distance algorithm, wherein thevalues of n₁ iterate over the range 0 . . . N₁ while the values of n₂iterate over the range N₂+1 . . . N₄ and subsequently the values of n₁iterate over the range N₁+1 . . . N₃ while the values of n₂ iterate overthe range 0 . . . N₄.
 6. The method of claim 1, further comprisingproviding a character weight to each character of the strings s₁ and s₂,wherein the association of the operation score to the first type editoperation comprises weighting the association score with the characterweight of the character involved in the first type edit operation. 7.The method of claim 5, further comprising providing a character weightto each character of the string s₁ and the string s₂, wherein theassociation of the operation score to the first type edit operationcomprises weighting the operation score with the character weight of thecharacter involved in the first type edit operation, wherein theassociation of the switching score to the first type edit operationcomprises weighting the switching score with a weight w_(c), the weightw_(c) being a predefined function of the combined score of a subsequenceof operations having as last operation said first type edit operationand the number of first type edit operations in said subsequence.
 8. Themethod of claim 7, the function being a ratio of the combined score andthe number of first type edit operations in said subsequence.
 9. Themethod of claim 1, the switching score being referred to as first typeswitching score, the distance algorithm being further configured for:associating to each first type edit operation of the sequence of editoperations a second type switching score if it is immediately precededin the sequence by a second type edit operation.
 10. The method of claim1, wherein the determining of the sequence of operations and theassociation of the operation scores and the deviation scores areperformed character wise in parallel.
 11. The method of claim 1, whereinN₁≥1 and N₂≥1, wherein the distance is converted into a similaritymeasure according to:${s_{gl} = {1. - \frac{d_{gl}\left( {s_{1},s_{2}} \right)}{{❘s_{1}❘} + {❘s_{2}❘} + {p\min\left( {{2{❘s_{1}❘}},{{❘s_{2}❘} - 1}} \right)}}}},$where p is the switching score, and d_(gl)(s₁, s₂) is the combinedscore.
 12. The method of claim 1, wherein the string s₁ is shorter thanstring s₂.
 13. The method of claim 6, wherein N₁≥1 and N₂≥1, wherein thesimilarity level is further determined according to:${s_{gl} = {1. - \frac{d_{gl}\left( {s_{1},s_{2}} \right)}{{❘s_{1}❘} + {❘s_{2}❘} + {p\overset{\_}{w}\min\left( {{2{❘s_{1}❘}},{{❘s_{2}❘} - 1}} \right)}}}},$where w is an average character weight of the strings s1 and s2, whered_(gl)(s₁, s₂) is the combined score and p is the switching score. 14.The method of claim 1, the switching score being smaller than the sum ofthe operation scores for one character insertion operation and onecharacter removal operation.
 15. The method of claim 1, the distancealgorithm being configured to determine the sequence of one or more editoperations by determining different candidate sequences of editoperations and selecting the candidate sequence that provides the lowestcombined score.
 16. A record matching method comprising comparing pairsof attributes values of two records using the method of claim 1resulting in individual similarity levels of the attributes andcombining the individual similarity levels for determining whether thetwo records are matching records.
 17. A computer program productcomprising a computer-readable storage medium having computer-readableprogram code embodied therewith, the computer-readable program codeconfigured to implement the method of claim
 1. 18. A computer system fordetermining a similarity between a string s₁ having N₁ characters, whereN₁≥0 and a string s₂ having N₂ characters, where N₂≥0, comprising: amemory; a processor; local data storage having stored thereon computerexecutable code, wherein the computer executable code includes theprogram instruction executable by a processor to cause the processor toperform a method, the method comprising: providing a distance algorithmbeing configured for: receiving a first string and a second string;determining a sequence of one or more edit operations to be performed oncharacters of the first string in order to obtain the second string, theedit operation being of a first type or a second type, the first typeedit operation comprising a character insertion operation or characterremoval operation, the second type edit operation comprising a charactermaintenance operation; wherein the first type edit operation isassociated with an operation score indicative of a cost for applying theedit operation; wherein the first type edit operation is associated witha switching score if it is immediately followed in the sequence by asecond type edit operation; combining the switching scores and/or theoperation scores associated with the sequence of edit operations,resulting in a combined score that is indicative of the similarity levelbetween the first and second strings; inputting first n₁ characters ofthe string s₁ as the first string and first n₂ characters of the strings₂ as the second string to the distance algorithm for obtaining thecombined score, wherein 0≤n₁≤N₁ and 0≤n₂≤N₂; repeatedly inputting thefirst n₁ characters of the string s₁ and first n₂ characters of thestring s₂ to the distance algorithm, wherein n₁ and n₂ are incrementedaccording to a nested loop, where n₁ represents the outer loop and n₂represents the inner loop; wherein the distance algorithm is configuredfor determining the sequence of edit operations in each iteration by:determining whether: a first combined score has been previouslydetermined for the first string having n₁−1 characters and the secondstring having n₂ characters using a first sequence of the editoperations, and/or a second combined score was previously determined forthe first string having n₁ characters and the second string having n₂−1characters using a second sequence of the edit operations, and/or athird combined score was previously determined, using a third sequenceof the edit operations, for the first string having n₁−1 characters andthe second string having n₂−1 characters, and the last character of thefirst string and the second string being the same; determining acombined score of the first, second and third combined scores if it isdetermined that it was not previously determined and selecting thelowest score of the determined combined scores; determining anadditional operation to be performed in addition to one of the first,second or third sequence of edit operations associated with the selectedlowest score in order to obtain the second string from the first string,wherein if the selected pair is (n₁, n₂−1), the additional operation isthe insertion operation, and if the selected pair is (n₁−1, n₂), theadditional operation is the removal operation and if the selected pairis (n₁−1, n₂−1), the additional operation is the maintenance operation;wherein the sequence of edit operations comprises the one of the first,second or third sequence of edit operations associated with the selectedlowest score and the determined additional operation; wherein thedistance algorithm is configured in each iteration for combining theswitching scores and/or the operation scores associated with thesequence of edit operations by combining the lowest score with theswitching score and/or the operation score associated with theadditional operation; and wherein the determining of the distancebetween the string s₁ and string s₂ is performed using the obtainedcombined score of the last iteration; and implementing at least oneaction by the computer system based upon the determined distance.